Algal blooms caused by eutrophication is one of the major environmental problems in China. Spectrum matching based on discrete particle swarm optimization (SMDPSO) algorithm was used to identify algal blooms in Hulun Lake, Inner Mongolia, and the classification results of floating algae index (FAI) were used as validation data to evaluate the accuracy of the algorithm. Then, the temporal and spatial characteristics of algal blooms from 2009 to 2018 were analyzed, after which the algorithm was applied to the identification of agal blooms in the Yellow Sea. The results show that SMDPSO algorithm can effectively identify algal blooms in Hulun Lake. The R2 and RMSE between SMDPSO and FAI are 0.97 and 0.22 km2 respectively. The outburst of algal blooms in Hulun Lake last from July to August, and mainly appeared at the edge of the lake. SMDPSO algorithm can not only extract the algal blooms (cyanobacteria is the dominant phylum) from Hulun Lake, but also identify enteromorpha (green algae) in the Yellow Sea. The algorithm shares the characteristics of high precision with spectral index method, and has the advantages of low cost, less parameters involved and no need of manual intervention. This study provides a novel tool for algal bloom remote sensing monitoring, which is helpful for controlling the eutrophication of lake water and improving the water ecological environment.